3 edition of Measurements & Estimations of Forest Stand Parameters Using Remote Sensing found in the catalog.
by Brill Academic Publishers
Written in English
|The Physical Object|
|Number of Pages||270|
In this paper, a literature overview is presented on the use of laser rangefinder techniques for the retrieval of forest inventory parameters and structural characteristics. The existing techniques are ordered with respect to their scale of application (i.e. spaceborne, airborne, and terrestrial laser scanning) and a discussion is provided on the efficiency, precision, and accuracy with which Cited by: The Journal of Applied Remote Sensing (JARS) is an online journal that optimizes the communication of concepts, information, and progress within the remote sensing community to improve the societal benefit for monitoring and management of natural disasters, weather forecasting, agricultural and urban land-use planning, environmental quality monitoring, ecological restoration, and numerous.
Introduction. Retrieving tropical forest stand structure parameters from remotely sensed data is of primary importance for estimating global carbon stocks in above‐ground biomass (Houghton et al. ; Grace ) as well as for obtaining large‐scale information required by regional biodiversity studies and forest type classification and mapping (Tuomisto et al. ).Cited by: Estimation of Forest Fuel Load From Radar Remote Sensing Semiempirical algorithms were developed to estimate crown and stem biomass and three major fuel load parameters, namely: 1) canopy fuel weight; 2) canopy bulk density; and 3) foliage moisture content. These estimates, when compared directly to measurements made at plot and stand Cited by:
good estimates of land cover, based on satellite or aerial sensing, but I will not cover those topics here. Literature Following is a short list of important papers covering methods for estimating forest biomass from tree inventory. The most recent papers (Chave et al. File Size: 2MB. Remote sensing–based forest aboveground biomass (AGB) estimation has been extensively explored in the past three decades, but how to effectively combine different sensor data and modeling algorithms is still poorly understood. This research conducted a comparative analysis of different datasets (e.g., Landsat Thematic [ ] Read more.
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Measurements and Estimations of Forest Stand Parameters Using Remote Sensing - CRC Press Book Generally speaking, the use of remote sensing imagery will reduce field work. Remote sensing imagery gives an overall view of the forest in addition to more detailed information, the extent of which depends mainly on scale and film used in case of analogue data, and mainly on pixel size in case of d.
This volume deals with measurements and estimations of forest stand parameters using aerial photographic, aircraft and satellite scanning data, radar and laser imagery. It includes technical and statistical information of practical examples from both temperate and tropical forests.
Read here ?book=[PDF Download] Measurements and Estimations of Forest Stand Parameters Using Remote Sensing. Estimating forest parameters using Landsat ETM+ spectral responses and monocultured plantation fieldwork measurements data. International Journal of Remote Sensing Cited by: 1.
Download PDF: Sorry, we are unable to provide the full text but you may find it at the following location(s): (external link); https://ris Author: D.A. Stellingwerf and Y.A. Hussin. Landsat data were able to predict stand age and tree density with R 2 =78% and R 2 =60%, respectively.
Mohamadi and shataee  investigated the possibility of estimation of tree diversity using Landsat ETM+ data in the Hyrcanian forests, northern of by: remote sensing applications involve estimation of either canopy cover (Gemmell ) or individual tree canopy area (Kalliovirta and Tokola ) as an intermediate stage in distinguishing the signals reflected from forest canopy and forest floor, after which, for instance, estimation of timber volume becomes possible (Bolduc et al.Maltamo.
A brief introduction is presented in the first section. The ground measurement techniques are described in the second section. It is then followed by various remote sensing methods for biomass estimation using optical remote sensing data (Section ), lidar. The air photo dot-count method is now widely and successfully used for estimating operational forest area in the USDA Forest Inventory and Analysis (FIA) program.
Possible alternatives that would provide for more frequent updates, spectral change detection, and maps of forest area include the AVHRR calibration center technique and various Cited by: System parameters (Sensor) All imaging RADAR sensors used for remote sensing are Synthetic Aperture Radars.
Mapping forest/land cover Mapping wetlands (inundated/flooded versus non- flooded) Mapping structural attributes (height, basal area, biomass, volume). Recently, numerous studies have attempted to determine forest height using remote sensing techniques that not only have the benefits of fast data acquisition, processing, and analysis but are also cost-effective.
However, if there was insufficient data to apply the latest remote sensing techniques, we need to consider many kinds of datasets as possible. In this study, we tried to determine Cited by: 2. 2 of 13 42 scale data acquisition, and cannot meet forestry production and ecological construction needs of the 43 time[4,5].With the advantages of that information can be collected quicker over larger geographical 44 areas at relatively lower costs, remote sensing technology has been widely used in the extraction of 45 forest composition and structural parameters, providing a strong Author: Shiqin Xie, Wei Wang, Qian liu, Jinghui Meng, Tianzhong Zhao, Guosheng Huang.
The various approaches attempt to stratify the forest by degradation type or intensity, or use proxies as an indicator of change. Forest disturbance, arising from logging, burning, disease or insect infestations, can be monitored by remote sensing approaches that detect changes in Cited by: A stand-level inventory is appropriate as a minimum unit for local and regional forest management.
South Korea currently produces a forest type map that contains only four categorical parameters. Stand height is a crucial forest attribute for understanding forest ecosystems that is currently missing and should be included in future forest type by: 5.
Estimation of forest stand volume, tree density and biodiversity using Landsat ETM + Data, comparison of linear and regression tree analyses. Estimation of forest attributes using remotely sensed data has being as a new potential for continuous management of natural resources.
The best stand-based accuracy using satellite sensor images was m−3 ha−1 (36%) RMSE for stand volume, m−3 ha−1 a−1 (49%) for annual increase in stand volume, where κ = Optical remote sensing data, with a variety of spatial and temporal resolutions, have been widely used for forest biomass estimation using different types of image processing techniques.
4, 7, 24, 29, 30, 84, 87, – For biomass estimation from optical data, the commonly used approaches are multiple regression analysis, k-nearest neighbor, and neural network.
24, 29, 30,Optical Cited by: The use of satellite-derived classification maps to improve post-stratified forest parameter estimates is well reducing the variance of post-stratification estimates for forest change parameters such as forest growth, it is logical to use a change-related strata map.
At the stand level, a time series of Landsat images isCited by: 6. [Read Book] Measurements and Estimations of Forest Stand Parameters Using Remote Sensing Read. Nehring. [Read Book] Measurements and Estimations of Forest Stand Parameters Using Remote Sensing Free. Winda. Applications of Remote Sensing for Crop Management - yield and protein estimation in wheat.
Canadian Forest Service scientists have a long history of pioneering research in the use of remote sensing technologies to gather forest information. Forester H.E.
Seely led the way in the s when he determined how to identify tree species and calculate timber volume using. Using the 30 cm images the deviations of the photogrammetrically estimated mean stand height amounted to m (%) on average, whereas using the 10 cm images the deviations amounted to m (%) compared to the field estimation.
The remote sensing data or derived forest attributes are commonly correlated to forest biomass using empirical regression models, non-parametric methods, and physically-based allometric models. Although forest biomass is widely estimated at various scales from remote sensing data, models tend to underestimate large biomass densities and Cited by: 5.satellite remote sensing to estimate forest biomass (Leboeuf et al., ) and to estimate diameter at breast height (dbh) and crown area (Greenberg et al., ).
Correlation of Shade with ground-measured stand characteristics has proven difﬁcult. Many remote-sensing studies use data such as.